Referring to FIG. 1, in a mobile telecommunication system, data is typically transmitted from a base station (Node B) 10 to wireless receiver (User Equipment UE) 12 through a propagation channel. However, a direct channel path 14 between the base station 10 and the receiver 12 is rare, because of signal reflectance from buildings 16, 18, vehicles etc. Instead, a signal transmitted by the base station 10, normally travels by a number of different paths 20, 22, 24, 26 to the receiver 12, wherein the paths introduce different degrees of attenuation and phase shift into the signal.
A channel equalizer builds an adaptive model (R) of a communications channel (whose characteristics represent those of all the signal pathways between a base station and a receiver) and inverts the model to regenerate an originally transmitted signal (x) from a received signal (h). To calculate the coefficients of an MMSE equalizer, it is necessary to solve a linear system whose size is at least equal to the channel length. This can be done, for example, by inverting the received signal covariance matrix (whose size is equal to the channel delay spread). However, these channel inversion calculations may consume most of the resources of a digital signal processor (DSP) chip in a wireless receiver. A Minimum Mean Squared Error (MMSE) channel equalizer is an optimal linear equalizer in terms of mean squared error (MSE). To avoid the above problem, the coefficients of a reduced-rank MMSE equalizer can be calculated by inverting a matrix whose size is less than that of the covariance matrix. The size of the smaller matrix is known as the “rank”. With this approach, the length of the equalizer remains the same, but the number of degrees of freedom to be optimized is reduced. The performance of a channel equalizer is dependent on its rank (or number of optimized coefficients in its channel model R). Reduced-rank MMSE equalizers where studied by S. Chowdhury et al. (in Proc. 43rd IEEE Midwest Symp. on Circuits and Systems, 2000). In these receivers, the number of taps to be optimized is limited to D (D<N). This allows a reduction in complexity and in some cases accelerated convergence.
HSDPA (High-Speed Downlink Packet Access) is an evolution of the third generation mobile telecommunications protocol UMTS (Universal mobile telecommunication system) which can achieve data rates of up to 14 mega bits per second (Mbps). However, even with reduced-rank MMSE equalizers, the increased data rates of the HSDPA protocol are proving difficult to achieve. French Patent Application FR0105268 (and S. Burykh and K. Abed-Meraim, EURASIP Journal on Applied Signal Processing 12 (2002), pp. 1387-1400), describe a method of adapting the rank of a reduced-rank filter to attain a target Signal to Interference plus Noise Ratio (SINR) in “short” code CDMA. However, in data packet networks (like HSDPA) the measure of performance is throughput (not SINR) and the codes are not “short” because of the presence of a scrambling code.
In addition to the above problem, since throughput depends on the detection of many symbols of a same packet, the throughput will flatten after a certain rank (known as the limit rank). Beyond this point, further increases in rank produce no increases in throughput. Thus, even if SINR continues to increase, throughput does not. Referring to the example depicted in FIG. 2, the equalizer has a limit rank of four. In other words, in this example, there is no need to increase the rank of the equalizer beyond four, because the throughput of the equalizer remains substantially the same even with further increases in rank.